Feasible Application of Shape Memory Alloy Plates in Steel Beam-Column Connections
Bibliographic record
Abstract
In the past earthquakes, steel moment resisting frames suffered damage. Earthquake-induced damage in the main structural members, such as beams, and columns, leads to permanent deformations in buildings. The resulting permanent damage following earthquakes substantially increases repair costs. The repair of damaged buildings with extensive permanent deformations may not be economically feasible. Therefore, it is essential to eliminate the residual deformations while designing ductile beam—to—column connections. The seismic performance of steel buildings can be improved by allocating pre-specified elements in the building so as to dissipate the input energy and also provide self-centering (the ability to return the structure to its undeformed position). This paper presents a feasibility study of utilizing superelastic Shape Memory Alloy (SMA) plates in steel beam—column connections. Three-dimensional finite element models of steel beam—column subassemblies are generated to assess the efficiency of SMA-plates on the seismic behavior of connections. In this new application, SMA-plates are used in the plastic hinge region of the beam. The results of these finite element simulations are promising. The proposed connections with SMA plates could return to their original positions, while exhibiting a ductile behavior with good energy dissipation. Furthermore, the occurrence of local buckling in beam flanges was prevented in the new connections with SMA-plates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".